Jan 12, 2023 · This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art …

Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.

In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep …

Jun 28, 2011 · In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to …

Feb 26, 2024 · The primary goal of multimodal deep learning is to train an end-to-end deep architecture that achieves high accuracy and effective fusion of information from different …

Jan 1, 2011 · In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train …

Multimodal Deep Learning is a machine learning subfield that aims to train AI models to process and find relationships between different types of data (modalities)—typically, images, video, …

Nov 9, 2017 · Abstract: The success of deep learning has been a catalyst to solving increasingly complex machine-learning problems, which often involve multiple data modalities.

May 1, 2024 · In this paper, we provide a comprehensive review of recent advances in multimodal hybrid deep learning, including a thorough analysis of the most commonly developed hybrid …

Multimodal deep learning is a subfield of machine learning where deep neural networks learn from multiple modalities of data (e.g., images, text, audio) simultaneously, instead of just one.